Scalable Online Top-N Recommender Systems

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Jorge, AM (Autor) FCUP; Vinagre, J (Autor) Outra; Domingues, M (Autor) Outra; João Gama (Autor) FEP; Carlos Soares (Autor) FEUP; Matuszyk, P (Autor) Outra; Spiliopoulou, M (Autor) Outra

Ata de Conferência Internacional

Título: E-COMMERCE AND WEB TECHNOLOGIES, EC-WEB 2016

Páginas: 3-20

17th International Conference on E-Commerce and Web Technologies (EC-Web)

Porto, PORTUGAL, SEP 05-08, 2016

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-00M-EY5

DOI: 10.1007/978-3-319-53676-7_1

Abstract (EN): Given the large volumes and dynamics of data that recommender systems currently have to deal with, we look at online stream based approaches that are able to cope with high throughput observations. In this paper we describe work on incremental neighborhood based and incremental matrix factorization approaches for binary ratings, starting with a general introduction, looking at various approaches and describing existing enhancements. We refer to recent work on forgetting techniques and multidimensional recommendation. We will also focus on adequate procedures for the evaluation of online recommender algorithms.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 18